Seq2seq Model Training with Global Semantic Loss
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Solution Overview
Problem
Conventional training of neural sequence-to-sequence (seq2seq) models focuses primarily on local loss, neglecting global or semantic properties, which can result in generated target sequences lacking desired properties such as consistency and semantic coherence, impacting applications like summarization and translation.
Innovation Solution
The method involves training seq2seq models to minimize both local cross-entropy loss and global semantic loss by using moment matching and cross-entropy-based gradient updates, incorporating feature functions to represent global features and constraints, and alternating between these training modes to optimize model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional training focuses primarily on local loss, then training simplicity is maintained, but global or semantic properties of generated sequences deteriorate
Solution Approach 1:
The training objective is segmented into two independent components: local cross-entropy loss for token-level accuracy and global semantic loss for sequence-level properties. Each component can be trained separately and combined through weighted summation, allowing the model to address both local and global requirements without overwhelming complexity
Solution Approach 2:
The patent introduces a weighting parameter lambda that controls the balance between local and global loss contributions. By adjusting this parameter, the training process can emphasize different aspects (local accuracy vs. global coherence) depending on the specific application requirements, providing flexible control over the trade-off
2Productivity
If only local cross-entropy loss is minimized, then training efficiency is maintained, but coherence and consistency of generated sequences deteriorate
Solution Approach 1:
The patent merges two training objectives (local cross-entropy loss and global semantic loss) into a unified training framework. The combined loss function integrates both components through weighted summation, allowing the model to simultaneously optimize for token-level accuracy and sequence-level coherence without sacrificing either efficiency or quality
Solution Approach 2:
The global semantic loss component provides feedback on sequence-level properties that are not captured by local loss alone. This feedback mechanism enables the model to learn from global coherence requirements and adjust its predictions accordingly, improving the overall quality of generated sequences
Data Source
AI summary
Methods for training a neural sequence-to-sequence (seq2seq) model. A processor receives the model and training data comprising a plurality of training source sequences and corresponding training target sequences, and generates corresponding predicted target sequences. Model parameters are updated based on a comparison of predicted target sequences to training target sequences to reduce or minimize both a local loss in the predicted target sequences and an expected loss of one or more global or semantic features or constraints between the predicted target sequences and the training target sequences given the training source sequences. Expected loss is based on global or semantic features or constraints of general target sequences given general source sequences.